How Data Teams Build Causal AI Models
transcript
show notes
Most data teams are stuck in correlation. They build models that predict what happens but can’t explain why, leading to costly mistakes when interventions change the underlying environment. In this episode, Lucas and Luna explore how forward-thinking organizations are shifting from predictive machine learning to causal inference. We look at a specific case where a major logistics provider used do-calculus and structural equation modeling to distinguish between weather-driven demand spikes and genuine marketing effectiveness. You’ll learn why standard A/B testing fails for complex business systems, how to build causal graphs before touching any code, and why understanding confounders is the only way to trust your model when the world shifts. This is Episode 181 of The Data Science Podcast with Fexingo.
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